<p>Exploring the impact of urban spatial environments on crime is a key topic in environmental criminology. Geographic big data provide solid support for environmental crime analysis at a micro-scale, especially the points of interest (POIs) and street view images (SVI). These data have also been used to explore the effects of urban facilities and street perception characteristics on crime. However, although urban facilities and street perception characteristics are not independent regarding their influence on crime, existing research often overlooks the interaction between these two features in their impact on crime. This study therefore aims to explore the joint effects of urban facilities and street perception characteristics on crime incidents. First, combinations of urban facilities representing spatial scenes of crime are discovered based on POIs. Then, street perception characteristics of the urban spatial environment are obtained based on SVI and deep learning techniques. On this basis, a quantitative framework for micro-scale urban spatial environments is constructed by integrating urban facilities and street perception characteristics. Finally, the geographically weighted regression model is used to explore the joint influence of urban facilities and street perception characteristics on crime. The experimental results show that the impact of urban facilities on crime incidents is greater than that of street perception characteristics. The influence of the same street perception characteristic on crime incidents varies depending on the type of urban facilities. Complex interaction effects are observed between urban facilities and street perception characteristics on crime.</p>